[ https://issues.apache.org/jira/browse/SPARK-13568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Nick Pentreath resolved SPARK-13568. ------------------------------------ Resolution: Fixed Fix Version/s: 2.2.0 > Create feature transformer to impute missing values > --------------------------------------------------- > > Key: SPARK-13568 > URL: https://issues.apache.org/jira/browse/SPARK-13568 > Project: Spark > Issue Type: New Feature > Components: ML > Reporter: Nick Pentreath > Assignee: yuhao yang > Priority: Minor > Fix For: 2.2.0 > > > It is quite common to encounter missing values in data sets. It would be > useful to implement a {{Transformer}} that can impute missing data points, > similar to e.g. {{Imputer}} in > [scikit-learn|http://scikit-learn.org/dev/modules/preprocessing.html#imputation-of-missing-values]. > Initially, options for imputation could include {{mean}}, {{median}} and > {{most frequent}}, but we could add various other approaches. Where possible > existing DataFrame code can be used (e.g. for approximate quantiles etc). -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org